为了实现脑机接口系统，对单次运动想象脑电信号的优化特征进行了提取与分类。针对运动想象脑电信号的特点，通过预处理得到脑电信号 Mu 节律成分，利用共空间模式算法在优化空间滤波下提取了运动想象脑电信号的特征。使用 Fisher 判别分析进行分类决策，并通过交叉验证与受试者操作曲线相结合的方法对分类性能进行综合评价。以交叉验证方式对应用空间滤波进行投影的特征维度确定问题进行深入讨论，评价结果表明本文方法在保证较高的准确率的同时可提高运行速度。基于优化的脑电特征进行运动想象意图分类，能够反映不同状态的差异并简化识别流程，为意图识别研究提供了一种准确高效的新方法。
In order to realize brain-computer interface (BCI), optimal features of single trail motor imagery electroencephalogram (EEG) were extracted and classified. Mu rhythm of EEG was obtained by preprocessing, and the features were optimized by spatial filtering, which are estimated from a set of data by method of common spatial pattern. Classification decision can be made by Fisher criterion, and classification performance can be evaluated by cross validation and receiver operating characteristic (ROC) curve. Optimal feature dimension determination projected by spatial filter was discussed deeply in cross-validation way. The experimental results show that the high discriminate accuracy can be guaranteed, meanwhile the program running speed is improved. Motor imagery intention classification based on optimized EEG feature provides difference of states and simplifies the recognition processing, which offers a new method for the research of intention recognition.
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